{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb02d39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e9b7f9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = []\n",
    "with open('trans_time.txt', 'rt') as f:\n",
    "    while (line := f.readline()) != '':\n",
    "        if line.startswith('time'):\n",
    "            times.append(float(line.split()[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b50f7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_avg_trans_time(filename):\n",
    "    times = []\n",
    "    with open(filename, 'rt') as f:\n",
    "        while (line := f.readline()) != '':\n",
    "            if line.startswith('time'):\n",
    "                times.append(float(line.split()[-1]))\n",
    "    return sum(times) / len(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c6d1f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0898"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_avg_trans_time('trans_time.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca7b7fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.242,\n",
       " 0.139,\n",
       " 0.214,\n",
       " 0.115,\n",
       " 0.048,\n",
       " 0.064,\n",
       " 0.11,\n",
       " 0.046,\n",
       " 0.047,\n",
       " 0.137,\n",
       " 0.149,\n",
       " 0.045,\n",
       " 0.041,\n",
       " 0.075,\n",
       " 0.111,\n",
       " 0.048,\n",
       " 0.092,\n",
       " 0.042,\n",
       " 0.043,\n",
       " 0.046,\n",
       " 0.062,\n",
       " 0.112,\n",
       " 0.049,\n",
       " 0.095,\n",
       " 0.142,\n",
       " 0.055,\n",
       " 0.252,\n",
       " 0.049,\n",
       " 0.095,\n",
       " 0.047,\n",
       " 0.081,\n",
       " 0.044,\n",
       " 0.075,\n",
       " 0.102,\n",
       " 0.047,\n",
       " 0.085,\n",
       " 0.13,\n",
       " 0.055,\n",
       " 0.057,\n",
       " 0.098,\n",
       " 0.047,\n",
       " 0.046,\n",
       " 0.233,\n",
       " 0.049,\n",
       " 0.059,\n",
       " 0.05,\n",
       " 0.182,\n",
       " 0.047,\n",
       " 0.096,\n",
       " 0.048,\n",
       " 0.043,\n",
       " 0.228,\n",
       " 0.136,\n",
       " 0.042,\n",
       " 0.056,\n",
       " 0.099,\n",
       " 0.043,\n",
       " 0.191,\n",
       " 0.104,\n",
       " 0.046,\n",
       " 0.086,\n",
       " 0.121,\n",
       " 0.044,\n",
       " 0.1,\n",
       " 0.055]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b167330d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/matplotlib/mpl-data/matplotlibrc\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.matplotlib_fname())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24f8c63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(times)\n",
    "x = np.array(range(1, len(times)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ec953915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'No.')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array(times)\n",
    "x = np.array(range(1, len(times)+1))\n",
    "plt.scatter(x, y)\n",
    "plt.title('RTT')\n",
    "plt.ylabel('Time/s')\n",
    "plt.xlabel('No.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8091bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "899898ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9c69b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c5986476",
   "metadata": {},
   "outputs": [],
   "source": [
    "yolov_times = []\n",
    "with open('sample.txt', 'rt') as f:\n",
    "    while (line := f.readline()) != '':\n",
    "        if line.startswith('yolov5'):\n",
    "            yolov_times.append(float(line.split()[-1][0:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "58f27af5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.501,\n",
       " 0.046,\n",
       " 0.053,\n",
       " 0.05,\n",
       " 0.05,\n",
       " 0.047,\n",
       " 0.047,\n",
       " 0.054,\n",
       " 0.051,\n",
       " 0.051,\n",
       " 0.047,\n",
       " 0.069,\n",
       " 0.071,\n",
       " 0.069,\n",
       " 0.066,\n",
       " 0.057,\n",
       " 0.052,\n",
       " 0.049,\n",
       " 0.048,\n",
       " 0.058,\n",
       " 0.055,\n",
       " 0.054,\n",
       " 0.051,\n",
       " 0.05,\n",
       " 0.058,\n",
       " 0.054,\n",
       " 0.057,\n",
       " 0.051,\n",
       " 0.051,\n",
       " 0.058,\n",
       " 0.06,\n",
       " 0.052,\n",
       " 0.052,\n",
       " 0.055,\n",
       " 0.053,\n",
       " 0.048,\n",
       " 0.053,\n",
       " 0.054,\n",
       " 0.161,\n",
       " 0.062,\n",
       " 0.066,\n",
       " 0.067,\n",
       " 0.065,\n",
       " 0.066,\n",
       " 0.067,\n",
       " 0.067,\n",
       " 0.067,\n",
       " 0.067,\n",
       " 0.05,\n",
       " 0.052,\n",
       " 0.052,\n",
       " 0.047,\n",
       " 0.051,\n",
       " 0.047,\n",
       " 0.055,\n",
       " 0.05,\n",
       " 0.055,\n",
       " 0.049,\n",
       " 0.048,\n",
       " 0.052,\n",
       " 0.057,\n",
       " 0.051,\n",
       " 0.048,\n",
       " 0.05,\n",
       " 0.058,\n",
       " 0.051,\n",
       " 0.051,\n",
       " 0.055,\n",
       " 0.051,\n",
       " 0.048,\n",
       " 0.052,\n",
       " 0.049,\n",
       " 0.05,\n",
       " 0.053,\n",
       " 0.053,\n",
       " 0.054,\n",
       " 0.054,\n",
       " 0.051,\n",
       " 0.048,\n",
       " 0.053,\n",
       " 0.054]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yolov_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c1b59af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.061185185185185204"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(yolov_times) / len(yolov_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32b2f7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'No.')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcBElEQVR4nO3dfZRcdZ3n8feHpsEGRhtIj0M6YCJiMApJsAdBZwdFJUEQIstoEBmXnVkOHhnE0WhydtcH1IE5kRl2VmY5rAJnEGEcjVkeJ8PigC4MDh2ChAjBCAx5QBIeIgI90Anf/ePehpuiqrqqup66f5/XOXW67kPd+tat2/dz7+93q0oRgZmZpWu3ThdgZmad5SAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8BaQtKjkj7Q6Tq6kaTbJP1pg489SNJzknqaXZely0FgVUm6WtLlJeOOkfSUpAPa8Pwh6fl85/ecpG+36HmulPT1Vix7IkoDNSIei4h9ImJnJ+tqRLeuY4PdO12Adb1zgXWSPhgRt0h6HfC/gc9FxONtqmFuRGxo03O1jCQB6nQdZqV8RmBVRcRTwJ8Bl0naG/gy8KuIuFLSSZLWSdqeN3e8rdwyJO0p6WJJW/LbxZL2zKc9IOnEwry7S3pS0hH11pofcV4i6UZJv5X0M0kHF6YfKukWSU9LWi/po/n4s4DTgS/kZx3XSzpT0vWFx26Q9P3C8EZJ8/L775Z0t6Tf5H/fXZjvNknfkHQH8ALw5pKaD5B0n6TPl3k9VwEHAdfndX1B0sz8LGn3wvK/LunOQu3752dyz+b1zBxvHVRYn/tJuiJ/z56RtLIw7b/k6+RpSddJmp6Pl6S/lrQ1Xx/3SXpHuXVc9c209ooI33wb9wb8ALgOeIps5/RW4Hngg0Av8AVgA7BHPv+jwAfy++cDdwG/CwwAdwJfy6d9Cbi68DwnAA8WhgPYAvwaWAHMrFLjlcDTwJFkZ7tXA9fm0/YGNgJn5tOOAJ4E3l547NcLy3ozsJ3sYOkA4N+AzYVpz+TT9svvn5Ev97R8eP983tuAx4C359N783F/CswEHgLOqvKaXlmP+fDMfJ3sXlj+BuBg4A3AL/JlfiB/vr8DrqhlHZR57huBvwf2zes+Jh9/bP64I4A9gf8J/CSftgBYDfSTnf28DTig3Dr2rXtuPiOwWn2abAdwfkQ8BnwMuDEibomIUeCbQB/w7jKPPT1/3NaI2AZ8lWzHCfA94CRJe+XDH8/HjTmGbOd3KFkg3DB2NFzBioj414jYQRYE8/LxJwKPRsQVEbEjIu4BfgicWm4hEfEw8Nv88ccAq4DNkg7Nh38aES+TBdcvI+KqfLnXAA8CHy4s7sqIWJdPH83HzSHbiX85Ii6r8npqcUVE/CoifgPcTHbG9n/zdfAPwPx610He/3M8cHZEPBMRoxFxez75dODyiLgnIl4ElgFH52ceo8DvkL1fiogHon1NiNYg9xFYTSLiCUlPAuvyUdPJjpLHpr8saSMwWObhu8yb35+eP26DpAeAD+fNBSfx6o6LiPhJfvclSZ8BniU7ylxbodRfF+6/AOyT338T8C5J2wvTdweuqrAcgNuB9wJvye9vJwuBo/Phcq9t7PUV18PGMss+nexI/gdVnr9WTxTuj5QZbmQdHAg8HRHPlJk2HbhnbCAinpP0FDAYET+W9C3gEuAgST8CPh8Rz9b5mqyNfEZgjdpCtmMBXukIPRDYPN68ZE1LWwrD15A1qZwM/CKqdwwHjXW4bgRuj4j+wm2fiPhUYbmlxoLgP+T3bycLgmN4NQhKXxtkr6+4Hsot+ytkzSvfU/VLQZv59cDjrYPSefeT1F9mWul7vzewP/lrjoi/iYh3kjWHvRVYks/qrzruUg4Ca9T3gRMkvV9SL/A54EWy9v9S1wD/TdKApGlk/QLfLUy/FjgO+BSFZiFJb5c0T1KPpH2Ai8h2Ng80UO8NwFslnSGpN7/9fqGD+wlKOnLJdvbvA/oiYhPwU2Ah2U5vTT7PTflyP553dH+MrNnnhnHqGQX+iKzd/ipJlf4Xy9XVqPHWwSvy5pybgb+VtG8+7x/mk78HnJm/N3sCfwH8LCIezZf3rnybeB74d2DsUtdmvhZrIgeBNSQi1gOfIOsofJKsTfzDEfFSmdm/DgwD95E16dyTjxtb1uPAv5D1L/x94XFvzIefBR4m6ys4sdDOXk+9vyULm8W82vn8l2SdnQDfAeYouwJqZf6Yh4DnyAKAvHnjYeCOyK/jj+yqqhPJgvApsk7zEyPiyRpqegk4hawT/fIKYXABWYhuL3dlUT1qWAelziALrAeBrcB5+XJuBf47Wf/C42Qd1Yvzx7ye7PLiZ8iayJ4i6z+CMuvYuoMifLZmZpYynxGYmSXOQWBmljgHgZlZ4hwEZmaJm3QfKJs2bVrMnDmz02WYmU0qq1evfjIiBspNm3RBMHPmTIaHhztdhpnZpCKp9BPwr3DTkJlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4ibdVUONWLlmM8tXrWfL9hGm9/exZMFsFs0v97X5ZmbpaekZgaSF+e+ibpC0tMz09+a/a3pvfvtSs2tYuWYzy1asZfP2EQLYvH2EZSvWsnJNua/NNzNLT8uCIP+xjUvIfu5uDnCapDllZv1pRMzLb+c3u47lq9YzMrpzl3EjoztZvmp9s5/KzGxSauUZwZHAhoh4OP/e9WvJfoGqrbZsH6lrvJlZaloZBIPs+lutmyj/e7ZHS/q5pJslvb3cgiSdJWlY0vC2bdvqKmJ6f19d483MUtPKICj3u7Klv4JzD/CmiJhL9ktXK8stKCIui4ihiBgaGCj7VRkVLVkwm77eXX8Stq+3hyULZte1HDOzqaqVQbCJ7MfMx8xg1x8sJyKejYjn8vs3Ab35b9o2zaL5g1xwymEM9vchYLC/jwtOOcxXDZmZ5Vp5+ejdwCGSZpH94Phi4OPFGST9HvBERISkI8mC6almF7Jo/qB3/GZmFbQsCCJih6RzgFVAD3B5RKyTdHY+/VLgVOBTknYAI8Di8I8om5m11aT78fqhoaHw11CbmdVH0uqIGCo3zV8xYWaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniWhoEkhZKWi9pg6SlVeb7fUk7JZ3aynrMzOy1WhYEknqAS4DjgTnAaZLmVJjvL4FVrarFzMwqa+UZwZHAhoh4OCJeAq4FTi4z358BPwS2trAWMzOroJVBMAhsLAxvyse9QtIg8BHg0moLknSWpGFJw9u2bWt6oWZmKWtlEKjMuCgZvhj4YkTsrLagiLgsIoYiYmhgYKBZ9ZmZGbB7C5e9CTiwMDwD2FIyzxBwrSSAacCHJO2IiJUtrMvMzApaGQR3A4dImgVsBhYDHy/OEBGzxu5LuhK4wSFgZtZeLQuCiNgh6Ryyq4F6gMsjYp2ks/PpVfsFzMysPVp5RkBE3ATcVDKubABExH9qZS1mZlaeP1lsZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeLGDQJJB0vaM7//XknnSuqvZeGSFkpaL2mDpKVlpp8s6T5J90oalvQHdb8CMzObkFrOCH4I7JT0FuA7wCzge+M9SFIPcAlwPDAHOE3SnJLZbgXmRsQ84D8D3669dDMza4ZaguDliNgBfAS4OCI+CxxQw+OOBDZExMMR8RJwLXBycYaIeC4iIh/cGwjMzKytagmCUUmnAZ8EbsjH9dbwuEFgY2F4Uz5uF5I+IulB4EayswIzM2ujWoLgTOBo4BsR8YikWcB3a3icyox7zRF/RPwoIg4FFgFfK7sg6ay8D2F427ZtNTy1mZnVqmIQSLpM0keAjRFxbkRcAxARj0TEhTUsexNwYGF4BrCl0swR8RPgYEnTyky7LCKGImJoYGCghqc2M7NaVTsjuByYC9wk6VZJX5Q0t45l3w0cImmWpD2AxcB1xRkkvUWS8vtHAHsAT9X1CszMbEJ2rzQhIu4C7gK+Iml/4Djgc5IOA9YA/xgR36/y+B2SzgFWAT3A5RGxTtLZ+fRLgf8I/LGkUWAE+Fih89jMzNpAjex3Jb0TWBgR32h+SdUNDQ3F8PBwu5/WzGxSk7Q6IobKTavlA2VvlPQdSTfnw3OAeZ0IATMza75arhq6kqx5Z3o+/BBwXovqMTOzNqslCKblfQEvQ9b2D+xsaVVmZtY2tQTB83lncQBIOgr4TUurMjOztql41VDBn5Nd9nmwpDuAAeDUllZlZmZtM24QRMQ9ko4BZpN9Wnh9RIy2vDIzM2uLcYMg/xbRDwEz8/mPk0RE/FWLazMzszaopWnoeuDfgbXkHcZmZjZ11BIEMyLi8JZXYmZmHVHLVUM3Szqu5ZWYmVlH1HJGcBfwI0m7AaNkHcYREa9vaWVmZtYWtQTBRWS/R7DWXwhnZjb11NI09EvgfoeAmdnUVMsZwePAbfmXzr04NtKXj5qZTQ21BMEj+W2P/GZmZlNILZ8s/mo7CjEzs86oGASSvhUR50i6nvI/On9SSyszM7O2qHZG8MfAOcA321SLmZl1QLUg+BVARNzeplrMzKwDqgXBgKQ/rzTRVw2ZmU0N1YKgB9iH7JPEZmY2RVULgscj4vy2VWJmZh1R7ZPFPhMwM0tAtSB4f9uqMDOzjqkYBBHxdDsLMTOzzqjlS+fMzGwKcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSWupUEgaaGk9ZI2SFpaZvrpku7Lb3dKmtvKeszM7LVaFgSSeoBLgOOBOcBpkuaUzPYIcExEHA58DbisVfWYmVl5rTwjOBLYEBEPR8RLwLXAycUZIuLOiHgmH7wLmNHCeszMrIxWBsEgsLEwvCkfV8mfADeXmyDpLEnDkoa3bdvWxBLNzKyVQVDuh22i7IzS+8iC4IvlpkfEZRExFBFDAwMDTSzRzMyq/VTlRG0CDiwMzwC2lM4k6XDg28DxEfFUC+sxM7MyWnlGcDdwiKRZkvYAFgPXFWeQdBCwAjgjIh5qYS1mZlZBy84IImKHpHOAVUAPcHlErJN0dj79UuBLwP7A30oC2BERQ62qyczMXksRZZvtu9bQ0FAMDw93ugwzs0lF0upKB9r+ZLGZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpa4Vn7XkNmUsXLNZpavWs+W7SNM7+9jyYLZLJpf7ct0zSYPB4HZOFau2cyyFWsZGd0JwObtIyxbsRbAYWBTgpuGzMaxfNX6V0JgzMjoTpavWt+hisyay0FgNo4t20fqGm822TgIzMYxvb+vrvFmk42DwGwcSxbMpq+3Z5dxfb09LFkwu0MVmTWXO4vNxjHWIeyrhmyqchCY1WDR/EHv+G3KctOQmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolraRBIWihpvaQNkpaWmX6opH+R9KKkz7eyFjMzK69lv1ksqQe4BPggsAm4W9J1EfGLwmxPA+cCi1pVh5mZVdfKM4IjgQ0R8XBEvARcC5xcnCEitkbE3cBoC+swM7MqWhkEg8DGwvCmfFzdJJ0laVjS8LZt25pSnJmZZVoZBCozLhpZUERcFhFDETE0MDAwwbLMzKyolUGwCTiwMDwD2NLC5zMzswa0MgjuBg6RNEvSHsBi4LoWPp+ZmTWgZVcNRcQOSecAq4Ae4PKIWCfp7Hz6pZJ+DxgGXg+8LOk8YE5EPNuquszMbFctCwKAiLgJuKlk3KWF+78mazIyM7MO8SeLzcwS5yAwM0ucg8DMLHEOAjOzxLW0s9gmZuWazSxftZ4t20eY3t/HkgWzWTS/oQ9nm5lVpIiGPuzbMUNDQzE8PNzpMlqiuON/Q18vz7+0g9Gdr74/vbuJfV63O9tfGHUwmFldJK2OiKGy0xwE3WHlms0sW7GWkdGdNT/GwdA9SkNcouL7Unqm975DB/jnB7eVHS5dVum8fs+tVg6CSeA9F/6YzdtHJrSMvt4eLjjlMO8Y2my8EC8GdrkzvYnwe261qhYE7iPoElsmGAIAI6M7Wb5qvXcKbVA8qt9NYmeVA6rRl4NnXsi+aX37SHO/cd3vuTWDg6BLTO/vm/AZATQnULpZN3Sgl54BVAuBdpjq77m1noOgSyxZMPs1zQuNNCkEWTNTvTvIZu5gqy2r3uep1oG+efsIy1asBWhrGCxftb6uvpxWm97f1+kS6jaR7a0bDgamGvcRNFkrN/DxrioqqqftuFwbd2kIVev8rHVZz7wwitj1Rymq1VlrB/pgfx93LD227ONb8V7MWnpjYz+s0QKt7CNoZqCXLrf0fa31ddT72E6GxngXBbQ7/NxZPI5mbSwT2cAbMVZ3pSalHomXI8Z9TfV2VFd7TY10elfakdezrMEyV9vUc/nteCFbfM2V6hpb38XgrKWOZl411KydT7VtGah7O6+lT6XSdlBUad2Xe2wj/4/17AuqretazuBrDbDxtsdaOQiqGO9ouJ5/pvF2EK26/K+WI9RqG04jR7iVXlOjW5OgacuqVbUdWzljO5tuOiqt5yyxtM5qdVXbliv1iVQ7M6v10ujBccKt2jZRug3Ve4A03vta77quVelrnuiZcCUOgioaOYIda94oPQptZE0Wl9XoDqLW11Bp5z3eVS9TWbUdWykBj1x4AtAd7dSNfPZkTP84ZyrNDPR6t69WXm5b7bkq1Tm2jZQ2a7a7jlLF7bEWDoIStR5htFujzUgT2SFMNsX+hnar9wis1Zrx2RObvJp5RpDcl86N7TQ3d1kIwKvXhNdr0fxBLjjlMAb7+xDZEUwjeiREdrTY29PYMooqLauRJYtsw1/+R3NZ86XjGGzzlTJ9vT0sWTC7rc85nsl+2Wij2+mYiW+hk1ezt8fkgqBdl/41ukNu9J970fxB7lh6LI9ceAIXfXQufb09dS/j5QgeufAE7v3ycSw/de4rwdLf18u+e/XW9ZoG+/vKLmuwv4+//tg8Hr3whJp35mPLumPpsa+cLS1ZMHvc19i7m16pu95wKz52sL+vKz+9267LRidycFFJX28PF310bsOBPrZN1PP4Zr+G8ZRuQ5846qCGX2+rt8fkPkdQbUcraEqbZLVOxfE04597bAOp9ZOv5Z570fzBhi/pLD1aqbSscp+dGG9ZxWUCdV0hU+ungSfSX9NO4332pJaO0/HU0kFe7cq1okqdtPX+jxS3iVq3oXouChirc7z/m0YvKql1v1DrVX/NkFwQVPoEb7G9rdJlYZu3j4zbYVTcSKvtrMotq5mne8WdbyM772rLhfp2wK1aVqWAqfZ8tV4hMhmUW3/17HzG65Qdb1uuZ2dead2WLrfey34b2YZqvSyz3DprxsUdtbzmdm+LyXUWT3QH0G0fEqlVM+ueKrrhyp92qucDixPdlifr/0Uz10kz62gGXzVUIrUdgJmZv320RL1NCmZmU1lyVw2ZmdmuHARmZolzEJiZJc5BYGaWOAeBmVniJt3lo5K2Af9Wx0OmAU+2qJyJcF31cV31cV31SaGuN0XEQLkJky4I6iVpuNK1s53kuurjuurjuuqTel1uGjIzS5yDwMwscSkEwWWdLqAC11Uf11Uf11WfpOua8n0EZmZWXQpnBGZmVoWDwMwscVM2CCQtlLRe0gZJSztYx+WStkq6vzBuP0m3SPpl/nffDtR1oKR/lvSApHWSPtMNtUl6naR/lfTzvK6vdkNdhfp6JK2RdEO31CXpUUlrJd0rabiL6uqX9ANJD+bb2dGdrkvS7Hw9jd2elXRep+vKa/tsvs3fL+ma/H+hLXVNySCQ1ANcAhwPzAFOkzSnQ+VcCSwsGbcUuDUiDgFuzYfbbQfwuYh4G3AU8Ol8HXW6theBYyNiLjAPWCjpqC6oa8xngAcKw91S1/siYl7hmvNuqOt/AP8YEYcCc8nWW0frioj1+XqaB7wTeAH4UafrkjQInAsMRcQ7gB5gcdvqiogpdwOOBlYVhpcByzpYz0zg/sLweuCA/P4BwPouWGf/B/hgN9UG7AXcA7yrG+oCZuT/jMcCN3TLewk8CkwrGdfRuoDXA4+QX5DSLXWV1HIccEc31AUMAhuB/ch+J+aGvL621DUlzwh4daWO2ZSP6xZvjIjHAfK/v9vJYiTNBOYDP6MLasubX+4FtgK3RERX1AVcDHwBeLkwrhvqCuCfJK2WdFaX1PVmYBtwRd6U9m1Je3dBXUWLgWvy+x2tKyI2A98EHgMeB34TEf/UrrqmahCozDhfJ1uGpH2AHwLnRcSzna4HICJ2RnbqPgM4UtI7OlwSkk4EtkbE6k7XUsZ7IuIIsqbQT0v6w04XRHZUewTwvyJiPvA8nWs2ew1JewAnAf/Q6VoA8rb/k4FZwHRgb0mfaNfzT9Ug2AQcWBieAWzpUC3lPCHpAID879ZOFCGplywEro6IFd1UG0BEbAduI+tj6XRd7wFOkvQocC1wrKTvdkFdRMSW/O9WsvbuI7ugrk3ApvxsDuAHZMHQ6brGHA/cExFP5MOdrusDwCMRsS0iRoEVwLvbVddUDYK7gUMkzcqTfzFwXYdrKroO+GR+/5Nk7fNtJUnAd4AHIuKvuqU2SQOS+vP7fWT/IA92uq6IWBYRMyJiJtn29OOI+ESn65K0t6TfGbtP1q58f6friohfAxslzc5HvR/4RafrKjiNV5uFoPN1PQYcJWmv/H/z/WSd6+2pq1MdNW3ofPkQ8BDwK+C/drCOa8ja/EbJjpL+BNifrNPxl/nf/TpQ1x+QNZfdB9yb3z7U6dqAw4E1eV33A1/Kx3d8nRVqfC+vdhZ3en29Gfh5fls3tq13uq68hnnAcP5ergT27ZK69gKeAt5QGNcNdX2V7KDnfuAqYM921eWvmDAzS9xUbRoyM7MaOQjMzBLnIDAzS5yDwMwscQ4CM7PEOQjM6iApJF1UGP68pK90sCSzCXMQmNXnReAUSdM6XYhZszgIzOqzg+x3ZD9bOkHSmyTdKum+/O9B7S/PrH4OArP6XQKcLukNJeO/BfxdRBwOXA38TdsrM2uAP1lsVgdJz0XEPpLOJ/vakBFgn4j4iqQnyb47fjT/Qr/HI8JNSNb1fEZg1piLyb43au8q8/goyyYFB4FZAyLiaeD7ZGEw5k6ybyYFOB34f+2uy6wRDgKzxl0EFJt+zgXOlHQfcAbZ7xsj6WxJZ3egPrOauI/AzCxxPiMwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxP1/pIYuMRN3aXsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array(yolov_times)\n",
    "x = np.array(range(1, len(yolov_times)+1))\n",
    "plt.scatter(x, y)\n",
    "plt.title('Yolov5 network time cost')\n",
    "plt.ylabel('Time/s')\n",
    "plt.xlabel('No.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fae9c44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
